Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations32428
Missing cells56941
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 MiB
Average record size in memory304.0 B

Variable types

Numeric18
DateTime2
Categorical15
Unsupported1
Text2

Alerts

points has constant value "0" Constant
advertisingdatacode is highly overall correlated with entry and 2 other fieldsHigh correlation
case is highly overall correlated with voucherHigh correlation
deliverytype is highly overall correlated with paymenttypeHigh correlation
entry is highly overall correlated with advertisingdatacode and 1 other fieldsHigh correlation
model is highly overall correlated with advertisingdatacode and 1 other fieldsHigh correlation
numberitems is highly overall correlated with weightHigh correlation
paymenttype is highly overall correlated with deliverytypeHigh correlation
voucher is highly overall correlated with advertisingdatacode and 1 other fieldsHigh correlation
w5 is highly overall correlated with weightHigh correlation
weight is highly overall correlated with numberitems and 1 other fieldsHigh correlation
title is highly imbalanced (94.0%) Imbalance
gift is highly imbalanced (95.8%) Imbalance
w8 is highly imbalanced (99.7%) Imbalance
delivpostcode has 31036 (95.7%) missing values Missing
advertisingdatacode has 25905 (79.9%) missing values Missing
w2 is highly skewed (γ1 = 21.10853315) Skewed
w3 is highly skewed (γ1 = 29.67460213) Skewed
w4 is highly skewed (γ1 = 39.12907294) Skewed
w6 is highly skewed (γ1 = 36.88827595) Skewed
w7 is highly skewed (γ1 = 89.95575121) Skewed
w10 is highly skewed (γ1 = 32.99162389) Skewed
customernumber has unique values Unique
delivpostcode is an unsupported type, check if it needs cleaning or further analysis Unsupported
domain has 1173 (3.6%) zeros Zeros
weight has 5126 (15.8%) zeros Zeros
remi has 31143 (96.0%) zeros Zeros
cancel has 30721 (94.7%) zeros Zeros
used has 31124 (96.0%) zeros Zeros
w0 has 15657 (48.3%) zeros Zeros
w1 has 26096 (80.5%) zeros Zeros
w2 has 28797 (88.8%) zeros Zeros
w3 has 32054 (98.8%) zeros Zeros
w4 has 31459 (97.0%) zeros Zeros
w5 has 28035 (86.5%) zeros Zeros
w6 has 31815 (98.1%) zeros Zeros
w7 has 31981 (98.6%) zeros Zeros
w9 has 29411 (90.7%) zeros Zeros
w10 has 30489 (94.0%) zeros Zeros

Reproduction

Analysis started2024-12-01 21:40:40.925114
Analysis finished2024-12-01 21:41:08.198893
Duration27.27 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

customernumber
Real number (ℝ)

Unique 

Distinct32428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33389.299
Minimum1
Maximum66251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:08.257962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3423.35
Q116802.75
median33552.5
Q350034.25
95-th percentile62969.3
Maximum66251
Range66250
Interquartile range (IQR)33231.5

Descriptive statistics

Standard deviation19148.09
Coefficient of variation (CV)0.57347987
Kurtosis-1.2055073
Mean33389.299
Median Absolute Deviation (MAD)16616
Skewness-0.017980104
Sum1.0827482 × 109
Variance3.6664937 × 108
MonotonicityNot monotonic
2024-12-01T22:41:08.377820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58849 1
 
< 0.1%
41191 1
 
< 0.1%
38860 1
 
< 0.1%
61917 1
 
< 0.1%
40647 1
 
< 0.1%
1347 1
 
< 0.1%
4686 1
 
< 0.1%
28710 1
 
< 0.1%
910 1
 
< 0.1%
50115 1
 
< 0.1%
Other values (32418) 32418
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
66251 1
< 0.1%
66250 1
< 0.1%
66249 1
< 0.1%
66247 1
< 0.1%
66246 1
< 0.1%
66245 1
< 0.1%
66244 1
< 0.1%
66241 1
< 0.1%
66240 1
< 0.1%
66234 1
< 0.1%

date
Date

Distinct351
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
Minimum2008-04-01 00:00:00
Maximum2009-03-31 00:00:00
2024-12-01T22:41:08.462197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:08.575135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

salutation
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
17840 
1
11614 
2
2974 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

Length

2024-12-01T22:41:08.663601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:08.726685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17840
55.0%
1 11614
35.8%
2 2974
 
9.2%

title
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
32202 
1
 
226

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

Length

2024-12-01T22:41:08.806142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:08.878121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32202
99.3%
1 226
 
0.7%

domain
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5171148
Minimum0
Maximum12
Zeros1173
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:08.939121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q311
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6839453
Coefficient of variation (CV)0.49007437
Kurtosis-1.0730758
Mean7.5171148
Median Absolute Deviation (MAD)3
Skewness-0.35693435
Sum243765
Variance13.571453
MonotonicityNot monotonic
2024-12-01T22:41:09.007148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12 7734
23.8%
9 6953
21.4%
4 6627
20.4%
8 3694
11.4%
11 1422
 
4.4%
5 1311
 
4.0%
2 1196
 
3.7%
0 1173
 
3.6%
1 1139
 
3.5%
6 548
 
1.7%
Other values (3) 631
 
1.9%
ValueCountFrequency (%)
0 1173
 
3.6%
1 1139
 
3.5%
2 1196
 
3.7%
3 381
 
1.2%
4 6627
20.4%
5 1311
 
4.0%
6 548
 
1.7%
7 113
 
0.3%
8 3694
11.4%
9 6953
21.4%
ValueCountFrequency (%)
12 7734
23.8%
11 1422
 
4.4%
10 137
 
0.4%
9 6953
21.4%
8 3694
11.4%
7 113
 
0.3%
6 548
 
1.7%
5 1311
 
4.0%
4 6627
20.4%
3 381
 
1.2%
Distinct275
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
Minimum2008-04-01 00:00:00
Maximum2008-12-31 00:00:00
2024-12-01T22:41:09.097019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:09.185028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

newsletter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
26932 
1
5496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

Length

2024-12-01T22:41:09.279323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:09.336411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26932
83.1%
1 5496
 
16.9%

model
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
1
18808 
3
7358 
2
6262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

Length

2024-12-01T22:41:09.408731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:09.479479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

Most occurring characters

ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 18808
58.0%
3 7358
 
22.7%
2 6262
 
19.3%

paymenttype
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
15063 
1
6549 
2
6537 
3
4279 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

Length

2024-12-01T22:41:09.553525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:09.627052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15063
46.5%
1 6549
20.2%
2 6537
20.2%
3 4279
 
13.2%

deliverytype
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
25879 
1
6549 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

Length

2024-12-01T22:41:09.703246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:09.761003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25879
79.8%
1 6549
 
20.2%

invoicepostcode
Real number (ℝ)

Distinct97
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.752282
Minimum0
Maximum99
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:09.842652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median47
Q366
95-th percentile91
Maximum99
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation24.361425
Coefficient of variation (CV)0.49969816
Kurtosis-0.75613171
Mean48.752282
Median Absolute Deviation (MAD)18
Skewness0.15775046
Sum1580939
Variance593.47905
MonotonicityNot monotonic
2024-12-01T22:41:09.938610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 1244
 
3.8%
50 1045
 
3.2%
45 1035
 
3.2%
52 917
 
2.8%
41 815
 
2.5%
22 811
 
2.5%
47 769
 
2.4%
30 722
 
2.2%
24 641
 
2.0%
40 605
 
1.9%
Other values (87) 23824
73.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 289
0.9%
2 56
 
0.2%
3 116
 
0.4%
4 259
0.8%
6 252
0.8%
7 104
 
0.3%
8 108
 
0.3%
9 125
 
0.4%
10 550
1.7%
ValueCountFrequency (%)
99 349
1.1%
98 45
 
0.1%
97 291
0.9%
96 236
0.7%
95 149
0.5%
94 124
 
0.4%
93 151
0.5%
92 118
 
0.4%
91 302
0.9%
90 315
1.0%

delivpostcode
Unsupported

Missing  Rejected  Unsupported 

Missing31036
Missing (%)95.7%
Memory size253.5 KiB

voucher
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
27174 
1
5254 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

Length

2024-12-01T22:41:10.010776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:10.089617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27174
83.8%
1 5254
 
16.2%

advertisingdatacode
Categorical

High correlation  Missing 

Distinct44
Distinct (%)0.7%
Missing25905
Missing (%)79.9%
Memory size253.5 KiB
BQ
2631 
AB
758 
CA
552 
BD
478 
AR
448 
Other values (39)
1656 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters13046
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowBR
2nd rowBQ
3rd rowBQ
4th rowBQ
5th rowAP

Common Values

ValueCountFrequency (%)
BQ 2631
 
8.1%
AB 758
 
2.3%
CA 552
 
1.7%
BD 478
 
1.5%
AR 448
 
1.4%
AX 423
 
1.3%
AQ 195
 
0.6%
BR 178
 
0.5%
AP 149
 
0.5%
BL 106
 
0.3%
Other values (34) 605
 
1.9%
(Missing) 25905
79.9%

Length

2024-12-01T22:41:10.152225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bq 2631
40.3%
ab 758
 
11.6%
ca 552
 
8.5%
bd 478
 
7.3%
ar 448
 
6.9%
ax 423
 
6.5%
aq 195
 
3.0%
br 178
 
2.7%
ap 149
 
2.3%
bl 106
 
1.6%
Other values (34) 605
 
9.3%

Most occurring characters

ValueCountFrequency (%)
B 4508
34.6%
Q 2826
21.7%
A 2783
21.3%
R 626
 
4.8%
C 583
 
4.5%
D 479
 
3.7%
X 427
 
3.3%
P 149
 
1.1%
L 108
 
0.8%
O 106
 
0.8%
Other values (14) 451
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 4508
34.6%
Q 2826
21.7%
A 2783
21.3%
R 626
 
4.8%
C 583
 
4.5%
D 479
 
3.7%
X 427
 
3.3%
P 149
 
1.1%
L 108
 
0.8%
O 106
 
0.8%
Other values (14) 451
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 4508
34.6%
Q 2826
21.7%
A 2783
21.3%
R 626
 
4.8%
C 583
 
4.5%
D 479
 
3.7%
X 427
 
3.3%
P 149
 
1.1%
L 108
 
0.8%
O 106
 
0.8%
Other values (14) 451
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 4508
34.6%
Q 2826
21.7%
A 2783
21.3%
R 626
 
4.8%
C 583
 
4.5%
D 479
 
3.7%
X 427
 
3.3%
P 149
 
1.1%
L 108
 
0.8%
O 106
 
0.8%
Other values (14) 451
 
3.5%

case
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
4
8648 
3
7125 
1
6349 
2
6230 
5
4076 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

Length

2024-12-01T22:41:10.235380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:10.307191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

Most occurring characters

ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 8648
26.7%
3 7125
22.0%
1 6349
19.6%
2 6230
19.2%
5 4076
12.6%

numberitems
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.019551
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:10.386295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum50
Range49
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7260456
Coefficient of variation (CV)0.854668
Kurtosis43.613822
Mean2.019551
Median Absolute Deviation (MAD)0
Skewness4.2415636
Sum65490
Variance2.9792335
MonotonicityNot monotonic
2024-12-01T22:41:10.475894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 17575
54.2%
2 6843
 
21.1%
3 3847
 
11.9%
4 1948
 
6.0%
5 930
 
2.9%
6 509
 
1.6%
7 284
 
0.9%
8 164
 
0.5%
9 106
 
0.3%
10 64
 
0.2%
Other values (17) 158
 
0.5%
ValueCountFrequency (%)
1 17575
54.2%
2 6843
 
21.1%
3 3847
 
11.9%
4 1948
 
6.0%
5 930
 
2.9%
6 509
 
1.6%
7 284
 
0.9%
8 164
 
0.5%
9 106
 
0.3%
10 64
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
27 4
< 0.1%
23 3
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 5
< 0.1%
19 4
< 0.1%
18 7
< 0.1%

gift
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
32280 
1
 
148

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

Length

2024-12-01T22:41:10.563515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:10.634681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32280
99.5%
1 148
 
0.5%

entry
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
18982 
1
13446 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

Length

2024-12-01T22:41:10.700476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:10.771318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

Most occurring characters

ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18982
58.5%
1 13446
41.5%

points
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
32428 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32428
100.0%

Length

2024-12-01T22:41:10.828199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:10.891037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32428
100.0%

Most occurring characters

ValueCountFrequency (%)
0 32428
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32428
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32428
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32428
100.0%

shippingcosts
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
27544 
1
4884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%

Length

2024-12-01T22:41:10.968745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:11.036173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27544
84.9%
1 4884
 
15.1%
Distinct471
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:11.231251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters324280
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st row2008-12-03
2nd row2008-12-30
3rd row2008-09-02
4th row2008-06-17
5th row2008-08-11
ValueCountFrequency (%)
2008-12-23 554
 
1.7%
2008-12-16 501
 
1.5%
2008-12-09 433
 
1.3%
2008-12-30 410
 
1.3%
2008-07-01 364
 
1.1%
2008-12-02 361
 
1.1%
2008-08-12 344
 
1.1%
2008-09-23 340
 
1.0%
2008-11-25 336
 
1.0%
2008-10-21 330
 
1.0%
Other values (461) 28455
87.7%
2024-12-01T22:41:11.525251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 100969
31.1%
- 64856
20.0%
2 51944
16.0%
8 36943
 
11.4%
1 31011
 
9.6%
9 9687
 
3.0%
7 6237
 
1.9%
4 6005
 
1.9%
5 5981
 
1.8%
6 5885
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 100969
31.1%
- 64856
20.0%
2 51944
16.0%
8 36943
 
11.4%
1 31011
 
9.6%
9 9687
 
3.0%
7 6237
 
1.9%
4 6005
 
1.9%
5 5981
 
1.8%
6 5885
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 100969
31.1%
- 64856
20.0%
2 51944
16.0%
8 36943
 
11.4%
1 31011
 
9.6%
9 9687
 
3.0%
7 6237
 
1.9%
4 6005
 
1.9%
5 5981
 
1.8%
6 5885
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 100969
31.1%
- 64856
20.0%
2 51944
16.0%
8 36943
 
11.4%
1 31011
 
9.6%
9 9687
 
3.0%
7 6237
 
1.9%
4 6005
 
1.9%
5 5981
 
1.8%
6 5885
 
1.8%
Distinct412
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:11.736812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters324280
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.2%

Sample

1st row2008-12-02
2nd row2009-02-03
3rd row2008-08-28
4th row0000-00-00
5th row2008-08-08
ValueCountFrequency (%)
0000-00-00 5472
 
16.9%
2008-12-16 351
 
1.1%
2008-12-17 252
 
0.8%
2008-12-18 246
 
0.8%
2008-12-19 227
 
0.7%
2008-12-09 222
 
0.7%
2008-12-29 220
 
0.7%
2008-12-22 219
 
0.7%
2008-11-24 214
 
0.7%
2008-12-12 214
 
0.7%
Other values (402) 24791
76.4%
2024-12-01T22:41:11.991601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 127681
39.4%
- 64856
20.0%
2 42713
 
13.2%
8 31513
 
9.7%
1 26371
 
8.1%
9 6766
 
2.1%
7 5308
 
1.6%
6 5180
 
1.6%
4 5125
 
1.6%
5 4762
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 127681
39.4%
- 64856
20.0%
2 42713
 
13.2%
8 31513
 
9.7%
1 26371
 
8.1%
9 6766
 
2.1%
7 5308
 
1.6%
6 5180
 
1.6%
4 5125
 
1.6%
5 4762
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 127681
39.4%
- 64856
20.0%
2 42713
 
13.2%
8 31513
 
9.7%
1 26371
 
8.1%
9 6766
 
2.1%
7 5308
 
1.6%
6 5180
 
1.6%
4 5125
 
1.6%
5 4762
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 127681
39.4%
- 64856
20.0%
2 42713
 
13.2%
8 31513
 
9.7%
1 26371
 
8.1%
9 6766
 
2.1%
7 5308
 
1.6%
6 5180
 
1.6%
4 5125
 
1.6%
5 4762
 
1.5%

weight
Real number (ℝ)

High correlation  Zeros 

Distinct2903
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637.92081
Minimum0
Maximum20076
Zeros5126
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.105374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median494
Q3920
95-th percentile1909
Maximum20076
Range20076
Interquartile range (IQR)917

Descriptive statistics

Standard deviation724.35813
Coefficient of variation (CV)1.1354985
Kurtosis30.957719
Mean637.92081
Median Absolute Deviation (MAD)443
Skewness3.1918519
Sum20686496
Variance524694.7
MonotonicityNot monotonic
2024-12-01T22:41:12.207372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5126
 
15.8%
1 1664
 
5.1%
2 1091
 
3.4%
3 284
 
0.9%
4 100
 
0.3%
960 48
 
0.1%
273 48
 
0.1%
369 48
 
0.1%
5 48
 
0.1%
205 47
 
0.1%
Other values (2893) 23924
73.8%
ValueCountFrequency (%)
0 5126
15.8%
1 1664
 
5.1%
2 1091
 
3.4%
3 284
 
0.9%
4 100
 
0.3%
5 48
 
0.1%
6 25
 
0.1%
7 14
 
< 0.1%
8 7
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
20076 1
< 0.1%
10690 1
< 0.1%
10234 1
< 0.1%
9942 1
< 0.1%
9633 1
< 0.1%
9451 1
< 0.1%
9171 1
< 0.1%
9049 1
< 0.1%
8863 1
< 0.1%
8823 1
< 0.1%

remi
Real number (ℝ)

Zeros 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05997903
Minimum0
Maximum19
Zeros31143
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.284281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38874047
Coefficient of variation (CV)6.481273
Kurtosis367.64134
Mean0.05997903
Median Absolute Deviation (MAD)0
Skewness14.092629
Sum1945
Variance0.15111915
MonotonicityNot monotonic
2024-12-01T22:41:12.358511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 31143
96.0%
1 949
 
2.9%
2 194
 
0.6%
3 71
 
0.2%
4 30
 
0.1%
5 20
 
0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
8 3
 
< 0.1%
14 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 31143
96.0%
1 949
 
2.9%
2 194
 
0.6%
3 71
 
0.2%
4 30
 
0.1%
5 20
 
0.1%
6 6
 
< 0.1%
7 7
 
< 0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 3
 
< 0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
5 20
0.1%
4 30
0.1%

cancel
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061613421
Minimum0
Maximum17
Zeros30721
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.423864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30683341
Coefficient of variation (CV)4.9799769
Kurtosis363.30343
Mean0.061613421
Median Absolute Deviation (MAD)0
Skewness11.637059
Sum1998
Variance0.094146741
MonotonicityNot monotonic
2024-12-01T22:41:12.505859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 30721
94.7%
1 1530
 
4.7%
2 121
 
0.4%
3 33
 
0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
17 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 30721
94.7%
1 1530
 
4.7%
2 121
 
0.4%
3 33
 
0.1%
4 9
 
< 0.1%
5 7
 
< 0.1%
6 4
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 4
 
< 0.1%
5 7
 
< 0.1%
4 9
 
< 0.1%
3 33
 
0.1%
2 121
 
0.4%
1 1530
 
4.7%
0 30721
94.7%

used
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068860244
Minimum0
Maximum19
Zeros31124
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.577705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47444436
Coefficient of variation (CV)6.8899605
Kurtosis340.82573
Mean0.068860244
Median Absolute Deviation (MAD)0
Skewness14.578823
Sum2233
Variance0.22509745
MonotonicityNot monotonic
2024-12-01T22:41:12.659313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 31124
96.0%
1 921
 
2.8%
2 181
 
0.6%
3 81
 
0.2%
4 51
 
0.2%
5 26
 
0.1%
6 15
 
< 0.1%
8 10
 
< 0.1%
7 6
 
< 0.1%
10 4
 
< 0.1%
Other values (7) 9
 
< 0.1%
ValueCountFrequency (%)
0 31124
96.0%
1 921
 
2.8%
2 181
 
0.6%
3 81
 
0.2%
4 51
 
0.2%
5 26
 
0.1%
6 15
 
< 0.1%
7 6
 
< 0.1%
8 10
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
13 2
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 2
 
< 0.1%
8 10
< 0.1%
7 6
< 0.1%

w0
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90212162
Minimum0
Maximum99
Zeros15657
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.760597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum99
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6547668
Coefficient of variation (CV)1.8343056
Kurtosis507.79518
Mean0.90212162
Median Absolute Deviation (MAD)1
Skewness13.718087
Sum29254
Variance2.7382531
MonotonicityNot monotonic
2024-12-01T22:41:12.839612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 15657
48.3%
1 10536
32.5%
2 3719
 
11.5%
3 1306
 
4.0%
4 574
 
1.8%
5 275
 
0.8%
6 126
 
0.4%
7 60
 
0.2%
8 38
 
0.1%
9 32
 
0.1%
Other values (26) 105
 
0.3%
ValueCountFrequency (%)
0 15657
48.3%
1 10536
32.5%
2 3719
 
11.5%
3 1306
 
4.0%
4 574
 
1.8%
5 275
 
0.8%
6 126
 
0.4%
7 60
 
0.2%
8 38
 
0.1%
9 32
 
0.1%
ValueCountFrequency (%)
99 1
 
< 0.1%
50 1
 
< 0.1%
45 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
40 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
32 1
 
< 0.1%
30 3
< 0.1%

w1
Real number (ℝ)

Zeros 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40434193
Minimum0
Maximum84
Zeros26096
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:12.936944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum84
Range84
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4103953
Coefficient of variation (CV)3.4881252
Kurtosis675.40801
Mean0.40434193
Median Absolute Deviation (MAD)0
Skewness17.292594
Sum13112
Variance1.9892148
MonotonicityNot monotonic
2024-12-01T22:41:13.035959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 26096
80.5%
1 3430
 
10.6%
2 1397
 
4.3%
3 801
 
2.5%
4 348
 
1.1%
5 141
 
0.4%
6 73
 
0.2%
7 40
 
0.1%
8 18
 
0.1%
10 11
 
< 0.1%
Other values (24) 73
 
0.2%
ValueCountFrequency (%)
0 26096
80.5%
1 3430
 
10.6%
2 1397
 
4.3%
3 801
 
2.5%
4 348
 
1.1%
5 141
 
0.4%
6 73
 
0.2%
7 40
 
0.1%
8 18
 
0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
84 1
 
< 0.1%
70 1
 
< 0.1%
42 1
 
< 0.1%
30 2
< 0.1%
29 3
< 0.1%
28 2
< 0.1%
27 1
 
< 0.1%
26 2
< 0.1%
25 2
< 0.1%
24 2
< 0.1%

w2
Real number (ℝ)

Skewed  Zeros 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27664364
Minimum0
Maximum90
Zeros28797
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.099530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum90
Range90
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3539805
Coefficient of variation (CV)4.8943129
Kurtosis917.74988
Mean0.27664364
Median Absolute Deviation (MAD)0
Skewness21.108533
Sum8971
Variance1.8332633
MonotonicityNot monotonic
2024-12-01T22:41:13.185897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 28797
88.8%
1 1755
 
5.4%
2 811
 
2.5%
3 379
 
1.2%
4 261
 
0.8%
5 146
 
0.5%
6 101
 
0.3%
7 59
 
0.2%
8 43
 
0.1%
9 18
 
0.1%
Other values (24) 58
 
0.2%
ValueCountFrequency (%)
0 28797
88.8%
1 1755
 
5.4%
2 811
 
2.5%
3 379
 
1.2%
4 261
 
0.8%
5 146
 
0.5%
6 101
 
0.3%
7 59
 
0.2%
8 43
 
0.1%
9 18
 
0.1%
ValueCountFrequency (%)
90 1
< 0.1%
56 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
40 1
< 0.1%
36 1
< 0.1%
30 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
27 2
< 0.1%

w3
Real number (ℝ)

Skewed  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018903417
Minimum0
Maximum15
Zeros32054
Zeros (%)98.8%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.255828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2535961
Coefficient of variation (CV)13.415358
Kurtosis1293.4263
Mean0.018903417
Median Absolute Deviation (MAD)0
Skewness29.674602
Sum613
Variance0.064310982
MonotonicityNot monotonic
2024-12-01T22:41:13.322519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 32054
98.8%
1 279
 
0.9%
2 45
 
0.1%
3 23
 
0.1%
4 11
 
< 0.1%
6 5
 
< 0.1%
5 3
 
< 0.1%
7 2
 
< 0.1%
14 2
 
< 0.1%
10 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 32054
98.8%
1 279
 
0.9%
2 45
 
0.1%
3 23
 
0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 2
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 5
 
< 0.1%
5 3
 
< 0.1%
4 11
< 0.1%
3 23
0.1%

w4
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04702726
Minimum0
Maximum36
Zeros31459
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.400922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43426508
Coefficient of variation (CV)9.2343266
Kurtosis2609.4227
Mean0.04702726
Median Absolute Deviation (MAD)0
Skewness39.129073
Sum1525
Variance0.18858616
MonotonicityNot monotonic
2024-12-01T22:41:13.486635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 31459
97.0%
1 711
 
2.2%
2 154
 
0.5%
3 53
 
0.2%
4 23
 
0.1%
5 10
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
12 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
ValueCountFrequency (%)
0 31459
97.0%
1 711
 
2.2%
2 154
 
0.5%
3 53
 
0.2%
4 23
 
0.1%
5 10
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
30 1
 
< 0.1%
26 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 5
< 0.1%
5 10
< 0.1%

w5
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18098557
Minimum0
Maximum14
Zeros28035
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.546079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56175089
Coefficient of variation (CV)3.1038435
Kurtosis48.985732
Mean0.18098557
Median Absolute Deviation (MAD)0
Skewness5.4052163
Sum5869
Variance0.31556406
MonotonicityNot monotonic
2024-12-01T22:41:13.612077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 28035
86.5%
1 3556
 
11.0%
2 499
 
1.5%
3 187
 
0.6%
4 78
 
0.2%
6 28
 
0.1%
5 28
 
0.1%
7 10
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 28035
86.5%
1 3556
 
11.0%
2 499
 
1.5%
3 187
 
0.6%
4 78
 
0.2%
5 28
 
0.1%
6 28
 
0.1%
7 10
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 10
 
< 0.1%
6 28
 
0.1%
5 28
 
0.1%
4 78
 
0.2%
3 187
 
0.6%
2 499
 
1.5%
1 3556
11.0%

w6
Real number (ℝ)

Skewed  Zeros 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027907981
Minimum0
Maximum27
Zeros31815
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.700074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29986188
Coefficient of variation (CV)10.744664
Kurtosis2490.5532
Mean0.027907981
Median Absolute Deviation (MAD)0
Skewness36.888276
Sum905
Variance0.089917146
MonotonicityNot monotonic
2024-12-01T22:41:13.766631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 31815
98.1%
1 472
 
1.5%
2 93
 
0.3%
3 23
 
0.1%
4 8
 
< 0.1%
5 5
 
< 0.1%
6 5
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
13 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 31815
98.1%
1 472
 
1.5%
2 93
 
0.3%
3 23
 
0.1%
4 8
 
< 0.1%
5 5
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 5
< 0.1%
5 5
< 0.1%
4 8
< 0.1%

w7
Real number (ℝ)

Skewed  Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023128161
Minimum0
Maximum55
Zeros31981
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:13.845797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40178211
Coefficient of variation (CV)17.371987
Kurtosis11341.795
Mean0.023128161
Median Absolute Deviation (MAD)0
Skewness89.955751
Sum750
Variance0.16142887
MonotonicityNot monotonic
2024-12-01T22:41:13.907542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 31981
98.6%
1 317
 
1.0%
2 78
 
0.2%
3 31
 
0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
55 1
 
< 0.1%
13 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 31981
98.6%
1 317
 
1.0%
2 78
 
0.2%
3 31
 
0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
25 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
6 2
 
< 0.1%
5 3
 
< 0.1%
4 11
 
< 0.1%
3 31
 
0.1%
2 78
0.2%

w8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
32422 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

Length

2024-12-01T22:41:13.989394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:14.056252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32422
> 99.9%
1 6
 
< 0.1%

w9
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16498088
Minimum0
Maximum48
Zeros29411
Zeros (%)90.7%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:14.118749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83670522
Coefficient of variation (CV)5.0715284
Kurtosis606.58
Mean0.16498088
Median Absolute Deviation (MAD)0
Skewness17.897094
Sum5350
Variance0.70007562
MonotonicityNot monotonic
2024-12-01T22:41:14.201580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 29411
90.7%
1 1999
 
6.2%
2 583
 
1.8%
3 213
 
0.7%
4 95
 
0.3%
5 39
 
0.1%
7 18
 
0.1%
6 18
 
0.1%
8 9
 
< 0.1%
11 7
 
< 0.1%
Other values (14) 36
 
0.1%
ValueCountFrequency (%)
0 29411
90.7%
1 1999
 
6.2%
2 583
 
1.8%
3 213
 
0.7%
4 95
 
0.3%
5 39
 
0.1%
6 18
 
0.1%
7 18
 
0.1%
8 9
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
48 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
26 2
 
< 0.1%
25 1
 
< 0.1%
20 3
< 0.1%
19 3
< 0.1%
17 2
 
< 0.1%
15 1
 
< 0.1%
14 5
< 0.1%

w10
Real number (ℝ)

Skewed  Zeros 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092882694
Minimum0
Maximum50
Zeros30489
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size253.5 KiB
2024-12-01T22:41:14.268006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum50
Range50
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61050914
Coefficient of variation (CV)6.5729052
Kurtosis2062.3783
Mean0.092882694
Median Absolute Deviation (MAD)0
Skewness32.991624
Sum3012
Variance0.37272142
MonotonicityNot monotonic
2024-12-01T22:41:14.349857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 30489
94.0%
1 1451
 
4.5%
2 288
 
0.9%
3 102
 
0.3%
4 38
 
0.1%
5 20
 
0.1%
6 13
 
< 0.1%
7 10
 
< 0.1%
10 3
 
< 0.1%
8 3
 
< 0.1%
Other values (9) 11
 
< 0.1%
ValueCountFrequency (%)
0 30489
94.0%
1 1451
 
4.5%
2 288
 
0.9%
3 102
 
0.3%
4 38
 
0.1%
5 20
 
0.1%
6 13
 
< 0.1%
7 10
 
< 0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
38 1
 
< 0.1%
27 1
 
< 0.1%
20 1
 
< 0.1%
17 1
 
< 0.1%
15 2
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
10 3
< 0.1%
9 2
< 0.1%

target90
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.5 KiB
0
26377 
1
6051 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32428
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Length

2024-12-01T22:41:14.434037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-01T22:41:14.487833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26377
81.3%
1 6051
 
18.7%

Interactions

2024-12-01T22:41:05.939117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.431821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.707339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.270464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.482976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.075462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.720422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.044971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.370257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.633774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.118358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.255771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.428741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.645717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.206914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.428219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.591731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.757930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.002624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.500707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.790484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.334501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.560264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.202331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.791102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.116523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.442711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.700071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.191312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.317823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.498004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.715720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.270385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.495894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.657621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.827629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.067696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.563171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.857869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.398107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.652561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.288502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.888036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.189830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.511155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.054024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.254582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.393246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.564270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.784176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.336344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.558705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.723567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.893006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.138747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.632168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.930846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.460156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.751456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.365104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.985752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.261601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.575256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.135958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.314538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.455419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.629571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.844112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.427837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.623917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.788182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.962201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.208520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.703004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.987066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.527153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.834239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.442854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.056813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.361975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.648970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.204683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.380225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.517056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.695782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.912461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.489062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.684459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.841749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.028445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.280427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.778301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.071158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.597964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.922852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.527616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.129683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.429608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.726693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.276423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.447803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.585393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.776602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.986420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.559438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.752735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.925388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.099048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.352601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.854930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.148202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.676792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.001931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.619714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.215394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.499161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.793070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.346251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.512332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.678596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.851533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.065807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.627175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.822868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.990940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.164569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.422870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:43.932144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.214775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.744791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.082609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.700886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.283895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.574949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.867605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.414449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.581931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.740120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.921445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.135673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.702039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.894497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.059636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.230100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.499022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.014964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.293542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.831462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.161719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.783037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.353581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.646046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.936873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.482154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.647934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.802710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.993032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.558281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.772629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.961958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.126296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.293664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.557588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.078962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.356585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.891383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.284150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.853742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.425705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.720035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.998574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.541480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.707569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.860518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.055285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.629322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.840027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.026738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.188631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.356176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.618481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.137768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.419096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.958022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.395104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.922220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.487331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.784369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.066534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.603249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.767336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.920296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.118283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.690829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.905037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.078225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.245863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.415821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.681166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.215899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.492726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.022464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.478791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:49.991902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.553923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.849476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.130283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.661359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.823029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.984298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.178246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.760098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.964713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.141386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.308083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.474710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.749949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.287024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.564142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.089221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.554510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.066421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.624830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:52.929782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.210934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.728186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.889721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.052717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.240587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.823000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.033897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.206740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.373912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.550435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.814744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.357586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.635206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.156815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.617830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.373807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.695485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.029600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.289603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.795538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.956540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.121169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.309767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.884187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.099100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.269732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.438511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.613660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:06.883281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.419057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.708669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.225288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.687314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.445313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.771111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.101452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.358388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.862088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.020944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.191897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.378498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:00.958070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.167626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.336008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.503964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.685249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:07.332151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.495698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:45.760130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.285653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.753643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.513215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.846269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.171008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.422597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.921165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.081354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.248579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.455334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.021080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.233024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.404386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.566202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.747236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:07.396142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.556156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.102872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.349889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.877311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.583792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.914236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.235545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.491320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:55.993762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.139222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.302522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.518401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.086728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.300350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.471782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.630439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.814321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:07.459048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:44.627274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:46.197803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:47.414970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:48.980117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:50.648695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:51.980111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:53.304191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:54.560836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:56.055524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:57.199380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:58.371248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:40:59.581901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:01.144902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:02.364195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:03.530469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:04.689768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-01T22:41:05.872794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-01T22:41:14.564817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
advertisingdatacodecancelcasecustomernumberdeliverytypedomainentrygiftinvoicepostcodemodelnewsletternumberitemspaymenttyperemisalutationshippingcoststarget90titleusedvoucherw0w1w10w2w3w4w5w6w7w8w9weight
advertisingdatacode1.0000.0000.3850.0010.1870.0490.5440.0000.0690.5710.1320.0860.1910.0620.1440.2830.1660.1530.0340.8210.0000.0000.0000.0000.1290.1210.0360.0000.0000.0000.0400.063
cancel0.0001.0000.023-0.0040.0000.0060.0210.0000.0020.0130.0000.0200.003-0.0160.0080.0090.0000.0000.0120.009-0.131-0.053-0.031-0.057-0.025-0.011-0.092-0.004-0.0010.0000.0370.029
case0.3850.0231.0000.0000.0870.0330.0950.0230.0500.1550.0380.1820.0650.0430.0850.4790.0540.0260.0390.5090.0630.0600.0270.0480.0160.0350.0510.0290.0120.0340.0450.193
customernumber0.001-0.0040.0001.0000.000-0.0030.0000.0090.0070.0000.0000.0020.0000.0040.0000.0110.0000.000-0.0010.000-0.0130.0040.0050.004-0.0050.0070.001-0.005-0.0040.0000.007-0.000
deliverytype0.1870.0000.0870.0001.0000.0330.3180.0330.0950.3600.0200.0001.0000.0070.0530.2120.0610.0040.0530.2210.0150.0070.0090.0130.0220.0130.0810.0120.0140.0000.0080.005
domain0.0490.0060.033-0.0030.0331.0000.0470.012-0.0080.0380.031-0.0230.0500.0030.1320.0150.0120.026-0.0190.051-0.001-0.0140.013-0.0210.0140.011-0.009-0.002-0.0020.0130.002-0.013
entry0.5440.0210.0950.0000.3180.0471.0000.0000.3230.9860.0620.0060.3210.0000.0980.1160.0410.0110.0910.0890.0070.0050.0100.0000.0350.0000.0980.0000.0050.0000.0000.032
gift0.0000.0000.0230.0090.0330.0120.0001.0000.0240.0000.0000.0000.0370.0000.0000.0240.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
invoicepostcode0.0690.0020.0500.0070.095-0.0080.3230.0241.0000.4290.043-0.0010.0670.0040.0280.0340.0190.014-0.0000.1760.0070.0080.010-0.0530.0080.0010.017-0.009-0.0090.0000.016-0.006
model0.5710.0130.1550.0000.3600.0380.9860.0000.4291.0000.0670.0210.2550.0000.0680.1220.0500.0080.0640.3200.0040.0040.0050.0040.0260.0000.0690.0000.0020.0010.0000.027
newsletter0.1320.0000.0380.0000.0200.0310.0620.0000.0430.0671.0000.0310.0210.0000.0600.0420.0830.0000.0000.0000.0000.0000.0000.0000.0140.0100.0250.0000.0000.0200.0270.032
numberitems0.0860.0200.1820.0020.000-0.0230.0060.000-0.0010.0210.0311.0000.0190.0440.0330.0760.0270.0000.0340.0420.3370.3950.0260.216-0.0460.077-0.2140.0480.0590.0000.0870.664
paymenttype0.1910.0030.0650.0001.0000.0500.3210.0370.0670.2550.0210.0191.0000.0120.1220.2160.0610.0340.0360.2310.0160.0080.0050.0170.0300.0330.0830.0290.0080.0270.0140.024
remi0.062-0.0160.0430.0040.0070.0030.0000.0000.0040.0000.0000.0440.0121.0000.0000.0210.0470.065-0.0100.0140.0270.0040.0240.0200.0120.011-0.0470.016-0.0040.0000.0320.039
salutation0.1440.0080.0850.0000.0530.1320.0980.0000.0280.0680.0600.0330.1220.0001.0000.0000.0310.0630.0180.0620.0330.0220.0100.0110.0180.0140.0260.0140.0090.0000.0090.029
shippingcosts0.2830.0090.4790.0110.2120.0150.1160.0240.0340.1220.0420.0760.2160.0210.0001.0000.0710.0010.0450.0930.0200.0170.0000.0100.0170.0100.0670.0400.0090.0000.0190.085
target900.1660.0000.0540.0000.0610.0120.0410.0000.0190.0500.0830.0270.0610.0470.0310.0711.0000.0000.0390.0290.0000.0070.0050.0000.0220.0000.0240.0100.0000.0000.0200.026
title0.1530.0000.0260.0000.0040.0260.0110.0000.0140.0080.0000.0000.0340.0650.0630.0010.0001.0000.0000.0040.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.000
used0.0340.0120.039-0.0010.053-0.0190.0910.000-0.0000.0640.0000.0340.036-0.0100.0180.0450.0390.0001.0000.0290.0310.031-0.0310.090-0.022-0.020-0.081-0.018-0.0200.000-0.0150.081
voucher0.8210.0090.5090.0000.2210.0510.0890.0060.1760.3200.0000.0420.2310.0140.0620.0930.0290.0040.0291.0000.0140.0140.0070.0110.0130.0000.0000.0000.0000.0000.0000.026
w00.000-0.1310.063-0.0130.015-0.0010.0070.0000.0070.0040.0000.3370.0160.0270.0330.0200.0000.0000.0310.0141.000-0.030-0.117-0.123-0.105-0.058-0.383-0.056-0.0520.000-0.1810.439
w10.000-0.0530.0600.0040.007-0.0140.0050.0000.0080.0040.0000.3950.0080.0040.0220.0170.0070.0000.0310.014-0.0301.000-0.040-0.100-0.053-0.019-0.193-0.012-0.0130.000-0.0560.359
w100.000-0.0310.0270.0050.0090.0130.0100.0000.0100.0050.0000.0260.0050.0240.0100.0000.0050.000-0.0310.007-0.117-0.0401.000-0.067-0.027-0.009-0.100-0.002-0.0040.000-0.057-0.002
w20.000-0.0570.0480.0040.013-0.0210.0000.000-0.0530.0040.0000.2160.0170.0200.0110.0100.0000.0000.0900.011-0.123-0.100-0.0671.000-0.038-0.044-0.140-0.044-0.0320.000-0.0910.218
w30.129-0.0250.016-0.0050.0220.0140.0350.0000.0080.0260.014-0.0460.0300.0120.0180.0170.0220.000-0.0220.013-0.105-0.053-0.027-0.0381.000-0.019-0.041-0.015-0.0130.000-0.035-0.158
w40.121-0.0110.0350.0070.0130.0110.0000.0000.0010.0000.0100.0770.0330.0110.0140.0100.0000.011-0.0200.000-0.058-0.019-0.009-0.044-0.0191.000-0.0690.0180.0810.000-0.0280.015
w50.036-0.0920.0510.0010.081-0.0090.0980.0000.0170.0690.025-0.2140.083-0.0470.0260.0670.0240.000-0.0810.000-0.383-0.193-0.100-0.140-0.041-0.0691.000-0.055-0.0470.000-0.126-0.577
w60.000-0.0040.029-0.0050.012-0.0020.0000.000-0.0090.0000.0000.0480.0290.0160.0140.0400.0100.000-0.0180.000-0.056-0.012-0.002-0.044-0.0150.018-0.0551.0000.0340.000-0.0230.001
w70.000-0.0010.012-0.0040.014-0.0020.0050.000-0.0090.0020.0000.0590.008-0.0040.0090.0090.0000.000-0.0200.000-0.052-0.013-0.004-0.032-0.0130.081-0.0470.0341.0000.000-0.021-0.003
w80.0000.0000.0340.0000.0000.0130.0000.0000.0000.0010.0200.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
w90.0400.0370.0450.0070.0080.0020.0000.0000.0160.0000.0270.0870.0140.0320.0090.0190.0200.000-0.0150.000-0.181-0.056-0.057-0.091-0.035-0.028-0.126-0.023-0.0210.0001.0000.116
weight0.0630.0290.193-0.0000.005-0.0130.0320.000-0.0060.0270.0320.6640.0240.0390.0290.0850.0260.0000.0810.0260.4390.359-0.0020.218-0.1580.015-0.5770.001-0.0030.0000.1161.000

Missing values

2024-12-01T22:41:07.590491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-01T22:41:07.929138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-01T22:41:08.139418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customernumberdatesalutationtitledomaindatecreatednewslettermodelpaymenttypedeliverytypeinvoicepostcodedelivpostcodevoucheradvertisingdatacodecasenumberitemsgiftentrypointsshippingcostsdeliverydatepromiseddeliverydaterealweightremicancelusedw0w1w2w3w4w5w6w7w8w9w10target90
0411912008-12-010092008-12-01022058NaN1NaN2201002008-12-032008-12-02737000110000000000
1388602008-12-161042008-12-16011134NaN0NaN2200002008-12-302009-02-03368020000000000000
2619172008-08-1900122008-08-19010051NaN0NaN1100012008-09-022008-08-2847000000000000100
3406472008-06-161082008-06-16010025NaN0NaN3200002008-06-170000-00-000000000002000000
413472008-08-080012008-08-08011141NaN0BR4200002008-08-112008-08-08843000002000000000
546862008-08-1000122008-08-10010095NaN0BQ3300012008-08-122008-08-111218003120000000000
6287102008-12-2100122008-12-21031178NaN0NaN4101002008-12-232008-12-221000100000000000
79102008-09-040092008-09-04031177NaN0NaN4201002008-09-052008-09-04238000200000000000
8501152008-06-2500122008-06-24013086NaN1NaN4400002008-06-262008-06-271577000400000000000
9521532008-08-0200122008-08-02110097NaN0NaN4200002008-08-052008-08-041237000000000000200
customernumberdatesalutationtitledomaindatecreatednewslettermodelpaymenttypedeliverytypeinvoicepostcodedelivpostcodevoucheradvertisingdatacodecasenumberitemsgiftentrypointsshippingcostsdeliverydatepromiseddeliverydaterealweightremicancelusedw0w1w2w3w4w5w6w7w8w9w10target90
32418156742008-11-010092008-11-01030073NaN0NaN4201002008-11-042008-11-035000200000000001
32419555182008-10-3020122008-10-30030019NaN0NaN5101002008-11-032008-10-311000002000000000
3242056862008-08-040042008-08-04031124NaN0NaN3101002008-08-052008-08-04279000100000000001
32421192972008-09-251082008-09-25010059NaN0BY3100012008-09-292008-09-26678000100000000000
32422380142008-07-1000122008-07-10031122NaN0NaN2101002008-07-112008-07-10557000100000000000
3242377842008-10-211082008-10-21012078NaN0NaN4400002008-10-222008-10-221343000140000000000
32424416952008-11-091042008-11-09013010NaN0NaN2100002008-11-110000-00-000000000001000001
3242576122008-04-122092008-04-12030055NaN0NaN2101012008-04-152008-04-14369000100000000000
32426319412008-11-1500122008-11-15010035NaN0NaN4200002008-11-182008-11-17558000200000000000
32427588492008-07-281052008-07-28022032NaN0NaN1101012008-08-122008-08-12208010000000000000